{"id":9708,"date":"2026-08-10T10:10:28","date_gmt":"2026-08-10T10:10:28","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-mastering-artificial-intelligence-ai-with-python-and-r-2024-6\/"},"modified":"2026-08-10T10:10:28","modified_gmt":"2026-08-10T10:10:28","slug":"udemy-mastering-artificial-intelligence-ai-with-python-and-r-2024-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-mastering-artificial-intelligence-ai-with-python-and-r-2024-6\/","title":{"rendered":"Udemy \u2013 Mastering Artificial Intelligence (AI) with Python and R 2024-6"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Mastering Artificial Intelligence (AI) with Python and R is a comprehensive program that guides participants from basic concepts to advanced techniques in the fields of artificial intelligence and machine learning. The course is designed with an emphasis on practical applications in data science and machine learning. At the beginning of the course, participants are introduced to how to set up a development environment using Anaconda Navigator and essential Python libraries. Then, the basic topics of Python programming are taught along with working with the NumPy library for numerical calculations, including array functions, indexing, and data selection. It then examines the importance and use of the Matplotlib and Seaborn libraries for data visualization, which play a vital role in interpreting and presenting data effectively. At the intermediate level, the course explores Python\u2019s role in machine learning and covers topics such as data preprocessing, the concept of bias versus variance, and model evaluation techniques. Participants will be introduced to the powerful Scikit-learn library for performing machine learning tasks, including loading and visualizing data, as well as applying dimensionality reduction methods such as principal component analysis (PCA). Additionally, widely used classification algorithms such as K-Nearest Neighbor (KNN) and Support Vector Machines (SVM) will be taught in detail, and participants will be enhanced in their ability to build and evaluate machine learning models.<\/span><\/p>\n

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The advanced part of the course focuses on predictive analytics using AI techniques in Python. Ensemble methods such as Random Forest and AdaBoost, how to deal with class imbalance, and hyperparameter tuning using network search are discussed. Participants apply these techniques to real-world scenarios, such as traffic forecasting using regression models. Unsupervised learning methods such as clustering (such as K-Means and Affinity Propagation) are also taught to discover patterns in unlabeled data. The section concludes with examples of classification tasks using algorithms such as logistic regression, Naive Bayes, and support vector machines (SVM).<\/span><\/p>\n

What you will learn:<\/span><\/strong><\/h3>\n